Genetics of aging, health, and survival: dynamic regulation of human longevity related traits

Yashin, Anatoliy I.; Wu, Deqing; Arbeeva, Liubov; Arbeev, Konstantin G.; Kulminski, Alexander M.; Akushevich, Igor; Kovtun, Mikhail; Culminskaya, Irina; Stallard, Eric; Li, Miaozhu; Ukraintseva, Svetlana V.

doi:10.3389/fgene.2015.00122

Cited by 28 publications

(26 citation statements)

References 73 publications

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“…The analyses highlighted the role of age in genetic regulation of BMI [29], sensitivity of the effects of longevity alleles to birth cohorts [30,31], sensitivity of genetic associations with lipids to chronological age [32,33], changes in the allele frequencies with age [34,35], antagonistic risks of diseases and death [16,32]. …”

Section: Discussionmentioning

confidence: 99%

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Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality

Kulminski

Culminskaya

et al. 2016

PLoS Genet

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Gaining insights into genetic predisposition to age-related diseases and lifespan is a challenging task complicated by the elusive role of evolution in these phenotypes. To gain more insights, we combined methods of genome-wide and candidate-gene studies. Genome-wide scan in the Atherosclerosis Risk in Communities (ARIC) Study (N = 9,573) was used to pre-select promising loci. Candidate-gene methods were used to comprehensively analyze associations of novel uncommon variants in Caucasians (minor allele frequency~2.5%) located in band 2q22.3 with risks of coronary heart disease (CHD), heart failure (HF), stroke, diabetes, cancer, neurodegenerative diseases (ND), and mortality in the ARIC study, the Framingham Heart Study (N = 4,434), and the Health and Retirement Study (N = 9,676). We leveraged the analyses of pleiotropy, age-related heterogeneity, and causal inferences. Meta-analysis of the results from these comprehensive analyses shows that the minor allele increases risks of death by about 50% (p = 4.6×10−9), CHD by 35% (p = 8.9×10−6), HF by 55% (p = 9.7×10−5), stroke by 25% (p = 4.0×10−2), and ND by 100% (p = 1.3×10−3). This allele also significantly influences each of two diseases, diabetes and cancer, in antagonistic fashion in different populations. Combined significance of the pleiotropic effects was p = 6.6×10−21. Causal mediation analyses show that endophenotypes explained only small fractions of these effects. This locus harbors an evolutionary conserved gene-desert region with non-coding intergenic sequences likely involved in regulation of protein-coding flanking genes ZEB2 and ACVR2A. This region is intensively studied for mutations causing severe developmental/genetic disorders. Our analyses indicate a promising target region for interventions aimed to reduce risks of many major human diseases and mortality.

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Section: Discussionmentioning

confidence: 99%

“…This heterogeneity can naturally contribute to non-replication of genetic effects in different populations even in case of populations of the same ancestry and phenotypes [16–18]. …”

Section: Introductionmentioning

confidence: 99%

Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality

Kulminski

Culminskaya

et al. 2016

PLoS Genet

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“…They have to be confirmed in the results of further genetic analyses of related traits in other datasets with better quality of genetic data. The issues of the quality of genotyping and methods of their testing were partly discussed in (Yashin et al 2015). …”

Section: Discussionmentioning

confidence: 99%

“…One may deal with the fact that models currently used in GWAS of human aging and longevity are oversimplified and do not correspond to inherent complexity of aging and longevity traits. Specifically, the traditional approaches to GWAS of these traits underestimate the possibility of pleiotropic associations of genetic variants with these traits: genes may show different associations with mortality and life span at different age intervals (Atzmon et al 2006; Yashin et al 1999; Yashin et al 2000); they may show pleiotropic effects on risks of distinct diseases (Yashin et al 2015). These effects may also include tradeoffs: genes may increase risk of one disease and reduce it for the next (Kulminski et al 2013; Ukraintseva et al 2010).…”

Section: Introductionmentioning

confidence: 99%

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

Yashin

Arbeev

et al. 2016

North American Actuarial Journal

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Background and Objective To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues. Data and Methods We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways. Results We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants. Conclusions . The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevit...

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“…Several are associated with a rare variant rs2229188 causing a change of Val19 to Ala. This SNP was found associated with HDL-C level, hypertension, and lifespan (Charlesworth et al, 2009;Han et al, 2013;Yashin et al, 2015). This missense mutation lies at the N-terminal part of the protein and is responsible for interaction with the membrane; therefore, it is not directly involved in enzymatic activity (Pikuleva and Waterman, 2013).…”

Section: Introductionmentioning

confidence: 99%

Role of Protein-Protein Interactions in Metabolism: Genetics, Structure, Function

Pandey¹,

Henderson²,

Ishii³

et al. 2018

Frontiers Research Topics

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Genetics of aging, health, and survival: dynamic regulation of human longevity related traits

Cited by 28 publications

References 73 publications

Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality

Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

Role of Protein-Protein Interactions in Metabolism: Genetics, Structure, Function

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